Voting for candidates: adapting data fusion techniques for an expert search task

In an expert search task, the users' need is to identify people who have relevant expertise to a topic of interest. An expert search system predicts and ranks the expertise of a set of candidate persons with respect to the users' query. In this paper, we propose a novel approach for predicting and ranking candidate expertise with respect to a query. We see the problem of ranking experts as a voting problem, which we model by adapting eleven data fusion techniques.We investigate the effectiveness of the voting approach and the associated data fusion techniques across a range of document weighting models, in the context of the TREC 2005 Enterprise track. The evaluation results show that the voting paradigm is very effective, without using any collection specific heuristics. Moreover, we show that improving the quality of the underlying document representation can significantly improve the retrieval performance of the data fusion techniques on an expert search task. In particular, we demonstrate that applying field-based weighting models improves the ranking of candidates. Finally, we demonstrate that the relative performance of the adapted data fusion techniques for the proposed approach is stable regardless of the used weighting models

Integrating multiple windows and document features for expert finding

Expert finding is a key task in enterprise search and has recently attracted lots of attention from both research and industry communities. Given a search topic, a prominent existing approach is to apply some information retrieval (IR) system to retrieve top ranking documents, which will then be used to derive associations between experts and the search topic based on cooccurrences. However, we argue that expert finding is more sensitive to multiple levels of associations and document features that current expert finding systems insufficiently address, including (a) multiple levels of associations between experts and search topics, (b) document internal structure, and (c) document authority. We propose a novel approach that integrates the above-mentioned three aspects as well as a query expansion technique in a two-stage model for expert finding. A systematic evaluation is conducted on TREC collections to test the performance of our approach as well as the effects of multiple windows, document features, and query expansion. These experimental results show that query expansion can dramatically improve expert finding performance with statistical significance. For three well-known IR models with or without query expansion, document internal structures help improve a single window-based approach but without statistical significance, while our novel multiple window-based approach can significantly improve the performance of a single window-based approach both with and without document internal structures

The voting model for people search

The thesis investigates how persons in an enterprise organisation can be ranked in response to a query, so that those persons with relevant expertise to the query topic are ranked first. The expertise areas of the persons are represented by documentary evidence of expertise, known as candidate profiles. The statement of this research work is that the expert search task in an enterprise setting can be successfully and effectively modelled using a voting paradigm. In the so-called Voting Model, when a document is retrieved for a query, this document represents a vote for every expert associated with the document to have relevant expertise to the query topic. This voting paradigm is manifested by the proposition of various voting techniques that aggregate the votes from documents to candidate experts. Moreover, the research work demonstrates that these voting techniques can be modelled in terms of a Bayesian belief network, providing probabilistic semantics for the proposed voting paradigm. The proposed voting techniques are thoroughly evaluated on three standard expert search test collections, deriving conclusions concerning each component of the Voting Model, namely the method used to identify the documents that represent each candidate's expertise areas, the weighting models that are used to rank the documents, and the voting techniques which are used to convert the ranking of documents into the ranking of experts. Effective settings are identified and insights about the behaviour of each voting technique are derived. Moreover, the practical aspects of deploying an expert search engine such as its efficiency and how it should be trained are also discussed. This thesis includes an investigation of the relationship between the quality of the underlying ranking of documents and the resulting effectiveness of the voting techniques. The thesis shows that various effective document retrieval approaches have a positive impact on the performance of the voting techniques. Interestingly, it also shows that a `perfect' ranking of documents does not necessarily translate into an equally perfect ranking of candidates. Insights are provided into the reasons for this, which relate to the complexity of evaluating tasks based on ranking aggregates of documents. Furthermore, it is shown how query expansion can be adapted and integrated into the expert search process, such that the query expansion successfully acts on a pseudo-relevant set containing only a list of names of persons. Five ways of performing query expansion in the expert search task are proposed, which vary in the extent to which they tackle expert search-specific problems, in particular, the occurrence of topic drift within the expertise evidence for each candidate. Not all documentary evidence of expertise for a given person are equally useful, nor may there be sufficient expertise evidence for a relevant person within an enterprise. This thesis investigates various approaches to identify the high quality evidence for each person, and shows how the World Wide Web can be mined as a resource to find additional expertise evidence. This thesis also demonstrates how the proposed model can be applied to other people search tasks such as ranking blog(ger)s in the blogosphere setting, and suggesting reviewers for the submitted papers to an academic conference. The central contributions of this thesis are the introduction of the Voting Model, and the definition of a number of voting techniques within the model. The thesis draws insights from an extremely large and exhaustive set of experiments, involving many experimental parameters, and using different test collections for several people search tasks. This illustrates the effectiveness and the generality of the Voting Model at tackling various people search tasks and, indeed, the retrieval of aggregates of documents in general

Using relevance feedback in expert search

In Enterprise settings, expert search is considered an important task. In this search task, the user has a need for expertise - for instance, they require assistance from someone about a topic of interest. An expert search system assists users with their "expertise need" by suggesting people with relevant expertise to the topic of interest. In this work, we apply an expert search approach that does not explicitly rank candidates in response to a query, but instead implicitly ranks candidates by taking into account a ranking of document with respect to the query topic. Pseudo-relevance feedback, aka query expansion, has been shown to improve retrieval performance in adhoc search tasks. In this work, we investigate to which extent query expansion can be applied in an expert search task to improve the accuracy of the generated ranking of candidates. We define two approaches for query expansion, one based on the initial of ranking of documents for the query topic. The second approach is based on the final ranking of candidates. The aims of this paper are two-fold. Firstly, to determine if query expansion can be successfully applied in the expert search task, and secondly, to ascertain if either of the two forms of query expansion can provide robust, improved retrieval performance. We perform a thorough evaluation contrasting the two query expansion approaches in the context of the TREC 2005 and 2006 Enterprise tracks

Modeling Documents as Mixtures of Persons for Expert Finding

In this paper we address the problem of searching for knowledgeable persons within the enterprise, known as the expert finding (or expert search) task. We present a probabilistic algorithm using the assumption that terms in documents are produced by people who are mentioned in them.We represent documents retrieved to a query as mixtures of candidate experts language models. Two methods of personal language models extraction are proposed, as well as the way of combining them with other evidences of expertise. Experiments conducted with the TREC Enterprise collection demonstrate the superiority of our approach in comparison with the best one among existing solutions

Entity Ranking on Graphs: Studies on Expert Finding

Todays web search engines try to offer services for finding various information in addition to simple web pages, like showing locations or answering simple fact queries. Understanding the association of named entities and documents is one of the key steps towards such semantic search tasks. This paper addresses the ranking of entities and models it in a graph-based relevance propagation framework. In particular we study the problem of expert finding as an example of an entity ranking task. Entity containment graphs are introduced that represent the relationship between text fragments on the one hand and their contained entities on the other hand. The paper shows how these graphs can be used to propagate relevance information from the pre-ranked text fragments to their entities. We use this propagation framework to model existing approaches to expert finding based on the entity's indegree and extend them by recursive relevance propagation based on a probabilistic random walk over the entity containment graphs. Experiments on the TREC expert search task compare the retrieval performance of the different graph and propagation models

Reply With: Proactive Recommendation of Email Attachments

Email responses often contain items-such as a file or a hyperlink to an external document-that are attached to or included inline in the body of the message. Analysis of an enterprise email corpus reveals that 35% of the time when users include these items as part of their response, the attachable item is already present in their inbox or sent folder. A modern email client can proactively retrieve relevant attachable items from the user's past emails based on the context of the current conversation, and recommend them for inclusion, to reduce the time and effort involved in composing the response. In this paper, we propose a weakly supervised learning framework for recommending attachable items to the user. As email search systems are commonly available, we constrain the recommendation task to formulating effective search queries from the context of the conversations. The query is submitted to an existing IR system to retrieve relevant items for attachment. We also present a novel strategy for generating labels from an email corpus---without the need for manual annotations---that can be used to train and evaluate the query formulation model. In addition, we describe a deep convolutional neural network that demonstrates satisfactory performance on this query formulation task when evaluated on the publicly available Avocado dataset and a proprietary dataset of internal emails obtained through an employee participation program.Comment: CIKM2017. Proceedings of the 26th ACM International Conference on Information and Knowledge Management. 201

Unsupervised, Efficient and Semantic Expertise Retrieval

We introduce an unsupervised discriminative model for the task of retrieving experts in online document collections. We exclusively employ textual evidence and avoid explicit feature engineering by learning distributed word representations in an unsupervised way. We compare our model to state-of-the-art unsupervised statistical vector space and probabilistic generative approaches. Our proposed log-linear model achieves the retrieval performance levels of state-of-the-art document-centric methods with the low inference cost of so-called profile-centric approaches. It yields a statistically significant improved ranking over vector space and generative models in most cases, matching the performance of supervised methods on various benchmarks. That is, by using solely text we can do as well as methods that work with external evidence and/or relevance feedback. A contrastive analysis of rankings produced by discriminative and generative approaches shows that they have complementary strengths due to the ability of the unsupervised discriminative model to perform semantic matching.Comment: WWW2016, Proceedings of the 25th International Conference on World Wide Web. 201